It has been shown that advancements in network architecture design have not improved the robustness of deep learning models to OOD data [1]}. Some studies have proposed methods that increase the robustness of deep learning models to OOD data. As an example, one study showed that some simple techniques such as histogram equalization and Adversarial Logit Pairing [2]} may improve robustness to perturbed and corrupted data. However, they noted that methods that work well on specific datasets may fail on other datasets. More importantly, these methods usually focus on in-distribution data that have been slightly perturbed, on which the model performance can be sub-optimal, and do not address the OOD samples, on which the model fails completely. Robustness against true OOD data has no meaning, and such data should be detected and reported/rejected.
